Drag Reducer Selection for Oil Pipeline Based Laboratory Experiment
Bibliographic record
Abstract
Oil and gas industry is one of the most capital-intensive industry in the world. Each step of oil and gas processing starting from exploration, exploitation, up to abandonment of the field, consumes large amount of capital. Optimization in each step of process is essential to reduce expenditure. In this paper, optimization of fluid flow in pipeline during oil transportation will be observed and studied in order to increase pipeline flow performance.This paper concentrates on chemical application into pipeline therefore the chemical can increase overall pipeline throughput or decrease energy requirement for oil transportation. These chemicals are called drag reducing agent, which consist of various chemicals such as surfactants, polymers, nanofluids, fibers, etc. During the application of chemical into pipeline flow system, these chemicals are already proven to decrease pump work for constant flow rate or allow pipeline to transport more oil for same amount of pump work. The first application of drag reducer in large scale oil transportation was in Trans Alaskan Pipeline System which cancel the need to build several pump stations because of the successful application. Since then, more company worldwide started to apply drag reducer to their pipeline system.Several tedious testings on laboratory should be done to examine the effect of drag reducer to crude oil that will be the subject of application. In this paper, one of the testing method is studied and experimented to select the most effective DRA from several proposed additives. For given pipeline system and crude oil type, the most optimum DRA is DRA A for pipeline section S-R and for section R-P is DRA B. Different type of oil and pipeline geometry will require different chemical drag reducer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".